This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Mathematical modeling of signal transduction pathways is used to understand the actions of growth factor receptors, to predict the consequences of exposure to therapeutic drugs, and to devise optimal treatments. Biochemically realistic models constructed to date suffer from the fact that few if any of the concentrations of key signaling proteins have been determined experimentally. Thus, concentrations must be estimated or fitted computationally, greatly limiting the ability to create accurate, predictive models. To determine the relative concentrations of signaling proteins in human breast cancer cell lines we will apply a novel mass spectrometry technique. Protein data will be compared to data from transcription profiles, enabling us to better understand the connection between mRNA and protein levels. Selected proteins and transcripts will also be measured using Western blotting and qPCR, while recombinant proteins will serve as absolute standards. Measured protein and mRNA levels will then be used to inform kinetic models with the specific goal of explaining cell-type specific signaling dynamics. The proposed pilot study will contribute to two related fields. For proteomics, the ability to measure the concentrations of multiple proteins and to understand the correlation between mRNA and protein levels will be a powerful tool. It will enable more general proteomic characterization of cell lines, tissues and primary cells, and reveal whether mRNA arrays and qPCR experiments are useful for understanding protein-based mechanisms. For modeling, quantifying the proteome will enable us decrease the number of free parameters and improve model identifiability. A full proteomic characterization might make it possible to predict behaviors in different cells on the basis of variability in protein levels alone. Taken together, these developments should accelerate our ability to predict the efficacy of drug action and ultimately to personalize therapeutic protocols.